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[optimize] optional parameters taken over to GPy

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Max Zwiessele 2016-04-08 07:37:51 +01:00
parent e5cdc06520
commit 61286a10ee
1 changed files with 6 additions and 4 deletions
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10
GPy/core/gp.py
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@ -532,21 +532,23 @@ class GP(Model):
def get_most_significant_input_dimensions(self, which_indices=None): def get_most_significant_input_dimensions(self, which_indices=None):
return self.kern.get_most_significant_input_dimensions(which_indices) return self.kern.get_most_significant_input_dimensions(which_indices)
def optimize(self, optimizer=None, start=None, **kwargs): def optimize(self, optimizer=None, start=None, messages=False, max_iters=1000, ipython_notebook=True, clear_after_finish=False, **kwargs):
""" """
Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors. Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.
kwargs are passed to the optimizer. They can be: kwargs are passed to the optimizer. They can be:
:param max_f_eval: maximum number of function evaluations :param max_iters: maximum number of function evaluations
:type max_f_eval: int :type max_iters: int
:messages: whether to display during optimisation :messages: whether to display during optimisation
:type messages: bool :type messages: bool
:param optimizer: which optimizer to use (defaults to self.preferred optimizer), a range of optimisers can be found in :module:`~GPy.inference.optimization`, they include 'scg', 'lbfgs', 'tnc'. :param optimizer: which optimizer to use (defaults to self.preferred optimizer), a range of optimisers can be found in :module:`~GPy.inference.optimization`, they include 'scg', 'lbfgs', 'tnc'.
:type optimizer: string :type optimizer: string
:param bool ipython_notebook: whether to use ipython notebook widgets or not.
:param bool clear_after_finish: if in ipython notebook, we can clear the widgets after optimization.
""" """
self.inference_method.on_optimization_start() self.inference_method.on_optimization_start()
try: try:
super(GP, self).optimize(optimizer, start, **kwargs) super(GP, self).optimize(optimizer, start, messages, max_iters, ipython_notebook, clear_after_finish, **kwargs)
except KeyboardInterrupt: except KeyboardInterrupt:
print("KeyboardInterrupt caught, calling on_optimization_end() to round things up") print("KeyboardInterrupt caught, calling on_optimization_end() to round things up")
self.inference_method.on_optimization_end() self.inference_method.on_optimization_end()